Hey everyone! I'm diving into using Amazon S3 to store various files, including text documents and images, but I'm a bit stumped on how to best set up querying to retrieve these files effectively. Ideally, I would build a front end that serves the right content on demand.
I've considered two approaches: One is to use custom metadata during upload, and then query based on that metadata. But I've read that I might need to use Athena and maintain a CSV inventory, which could be cumbersome considering I may have thousands of files.
The other idea is to name the uploaded files in a way that makes them easily retrievable, but I'm not entirely sure how to implement that.
My ultimate goal is to quickly find and serve the correct objects from S3 using a Python API, even when I don't have all the necessary namespaces. Any advice on how to streamline this process would be super helpful! Thank you!
5 Answers
Naming files in a way that your API can easily access them is actually quite common! By adopting a consistent naming convention, your application can either use the exact filename or search using a low cardinality prefix that enables quick searches via the AWS listObjects API.
If you're looking at indexing, it might be helpful to clarify how you plan to index all this data, as different methods come with their own pros and cons.
Have you thought about storing the metadata about your objects in a database along with their URLs? You could then query that database to get the corresponding S3 object. This can streamline access to your files without the hassle of large CSV inventories.
But won't that just delay access or require me to maintain a massive CSV file?
Could you give more details on your use case? I get that you're using S3 for storage, but how are you organizing those files? Maybe by client, user, or date? Knowing what you're querying for and how many files you anticipate can really help.
As a general suggestion, consider using an indexed database that can search and retrieve files based on your criteria. Relying solely on metadata could be tricky since you need to know the object name first to query it. Making an inventory report can be useful, but that's a delayed approach.
The use case is pretty much about storing documents like scientific papers or patents in S3, and I need to be able to query them to find the exact files easily. Since each document has unique identifiers (like who it belongs to), searching for specific types can be a challenge, like querying all engineering patents. So, I'm looking for the best way to achieve this!
From my experience managing a content platform with a huge volume of objects, querying S3 can be difficult. Athena can be too slow for real-time requests. A solid approach is to index metadata in DynamoDB upon upload, partitioning by customer ID and sorting by timestamp. This way, you get instant queries for minimal cost.
In fact, I just did a proof of concept between two performance platforms and found a significant cost savings by adjusting our S3 inventory reporting—definitely something to consider!
Thanks for the tip! We won't have as many files, but there are still so many ways to build a solution. I’m used to Azure, and switching to AWS has been a bit of a learning curve for me. I'll definitely add that strategy to my list!
You might also consider indexing your objects in DynamoDB for simple keyword searches. If your users need more advanced search capabilities, using OpenSearch or Elasticsearch could be the way to go, allowing for more powerful querying of your content.
That’s what I’m curious about! What are some effective indexing strategies, since I'm not very experienced with AWS yet?